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run_lookups_fs.py
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Wed, Oct 2, 07:45
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text/x-python
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Fri, Oct 4, 07:45 (1 d, 22 h)
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R4670 PySONIC (old)
run_lookups_fs.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Date: 2017-06-02 17:50:10
# @Email: theo.lemaire@epfl.ch
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-03-14 23:38:55
''' Create lookup table for specific neuron. '''
import
os
import
pickle
import
logging
import
numpy
as
np
from
argparse
import
ArgumentParser
from
PySONIC.utils
import
logger
,
getNeuronLookupsFile
from
PySONIC.batches
import
createQueue
,
runBatch
from
PySONIC.neurons
import
getNeuronsDict
from
PySONIC.core
import
NeuronalBilayerSonophore
# Default parameters
defaults
=
dict
(
neuron
=
'RS'
,
radius
=
32.0
,
# nm
freq
=
500
,
# kHz
amp
=
np
.
insert
(
np
.
logspace
(
np
.
log10
(
0.1
),
np
.
log10
(
600
),
num
=
50
),
0
,
0.0
),
# kPa
)
def
computeAStimLookups
(
neuron
,
a
,
Fdrive
,
Aref
,
Qref
,
fsref
,
mpi
=
False
,
loglevel
=
logging
.
INFO
):
# Check validity of input parameters
for
key
,
values
in
{
'coverage fractions'
:
fsref
,
'amplitudes'
:
Aref
}
.
items
():
if
not
(
isinstance
(
values
,
list
)
or
isinstance
(
values
,
np
.
ndarray
)):
raise
TypeError
(
'Invalid {} (must be provided as list or numpy array)'
.
format
(
key
))
if
not
all
(
isinstance
(
x
,
float
)
for
x
in
values
):
raise
TypeError
(
'Invalid {} (must all be float typed)'
.
format
(
key
))
if
len
(
values
)
==
0
:
raise
ValueError
(
'Empty {} array'
.
format
(
key
))
if
key
is
'coverage fractions'
and
min
(
values
)
<=
0
:
raise
ValueError
(
'Invalid {} (must all be strictly positive)'
.
format
(
key
))
if
key
is
'amplitudes'
and
min
(
values
)
<
0
:
raise
ValueError
(
'Invalid {} (must all be positive or null)'
.
format
(
key
))
# populate inputs dictionary
inputs
=
dict
(
fs
=
fsref
,
# (-)
A
=
Aref
,
# Pa
Q
=
Qref
# C/m2
)
# create simulation queue
nA
,
nQ
,
nfs
=
len
(
Aref
),
len
(
Qref
),
len
(
fsref
)
queue
=
createQueue
(([
Fdrive
],
Aref
,
Qref
,
fsref
))
# run simulations and populate outputs (list of lists)
logger
.
info
(
'Starting simulation batch for
%s
neuron'
,
neuron
.
name
)
nbls
=
NeuronalBilayerSonophore
(
a
,
neuron
)
outputs
=
runBatch
(
nbls
,
'computeEffVars'
,
queue
,
mpi
=
mpi
,
loglevel
=
loglevel
)
outputs
=
np
.
array
(
outputs
)
.
T
# Split comp times and lookups
tcomps
=
outputs
[
0
]
outputs
=
outputs
[
1
:]
# Reshape comp times into 4D array
tcomps
=
tcomps
.
reshape
(
nA
,
nQ
,
nfs
)
# reshape outputs into 4D arrays and add them to lookups dictionary
logger
.
info
(
'Reshaping output into lookup tables'
)
keys
=
[
'V'
,
'ng'
]
+
neuron
.
rates
assert
len
(
keys
)
==
len
(
outputs
),
'Lookup keys not matching array size'
lookups
=
{}
for
key
,
output
in
zip
(
keys
,
outputs
):
lookups
[
key
]
=
output
.
reshape
(
nA
,
nQ
,
nfs
)
# Store inputs, lookup data and comp times in dictionary
df
=
{
'input'
:
inputs
,
'lookup'
:
lookups
,
'tcomp'
:
tcomps
}
return
df
def
main
():
ap
=
ArgumentParser
()
# Runtime options
ap
.
add_argument
(
'--mpi'
,
default
=
False
,
action
=
'store_true'
,
help
=
'Use multiprocessing'
)
ap
.
add_argument
(
'-v'
,
'--verbose'
,
default
=
False
,
action
=
'store_true'
,
help
=
'Increase verbosity'
)
ap
.
add_argument
(
'-t'
,
'--test'
,
default
=
False
,
action
=
'store_true'
,
help
=
'Test configuration'
)
# Stimulation parameters
ap
.
add_argument
(
'-n'
,
'--neuron'
,
type
=
str
,
default
=
defaults
[
'neuron'
],
help
=
'Neuron name (string)'
)
ap
.
add_argument
(
'-a'
,
'--radius'
,
type
=
float
,
default
=
defaults
[
'radius'
],
help
=
'Sonophore radius (nm)'
)
ap
.
add_argument
(
'-f'
,
'--freq'
,
type
=
float
,
default
=
defaults
[
'freq'
],
help
=
'US frequency (kHz)'
)
ap
.
add_argument
(
'-A'
,
'--amp'
,
nargs
=
'+'
,
type
=
float
,
help
=
'Acoustic pressure amplitude (kPa)'
)
# Parse arguments
args
=
{
key
:
value
for
key
,
value
in
vars
(
ap
.
parse_args
())
.
items
()
if
value
is
not
None
}
loglevel
=
logging
.
DEBUG
if
args
[
'verbose'
]
is
True
else
logging
.
INFO
logger
.
setLevel
(
loglevel
)
mpi
=
args
[
'mpi'
]
neuron_str
=
args
[
'neuron'
]
a
=
args
[
'radius'
]
*
1e-9
# m
Fdrive
=
args
[
'freq'
]
*
1e3
# Hz
amps
=
np
.
array
(
args
.
get
(
'amp'
,
defaults
[
'amp'
]))
*
1e3
# Pa
fs
=
np
.
linspace
(
0
,
100
,
101
)
*
1e-2
# (-)
# Check neuron name validity
if
neuron_str
not
in
getNeuronsDict
():
logger
.
error
(
'Unknown neuron type: "
%s
"'
,
neuron_str
)
return
neuron
=
getNeuronsDict
()[
neuron_str
]()
charges
=
np
.
arange
(
neuron
.
Qbounds
()[
0
],
neuron
.
Qbounds
()[
1
]
+
1e-5
,
1e-5
)
# C/m2
if
args
[
'test'
]:
fs
=
np
.
array
([
fs
.
min
(),
fs
.
max
()])
amps
=
np
.
array
([
amps
.
min
(),
amps
.
max
()])
charges
=
np
.
array
([
charges
.
min
(),
0.
,
charges
.
max
()])
# Check if lookup file already exists
lookup_path
=
getNeuronLookupsFile
(
neuron
.
name
,
a
=
a
,
Fdrive
=
Fdrive
,
fs
=
True
)
if
os
.
path
.
isfile
(
lookup_path
):
logger
.
warning
(
'"
%s
" file already exists and will be overwritten. '
+
'Continue? (y/n)'
,
lookup_path
)
user_str
=
input
()
if
user_str
not
in
[
'y'
,
'Y'
]:
logger
.
error
(
'
%s
Lookup creation canceled'
,
neuron
.
name
)
return
# compute lookups
df
=
computeAStimLookups
(
neuron
,
a
,
Fdrive
,
amps
,
charges
,
fs
,
mpi
=
mpi
,
loglevel
=
loglevel
)
# Save dictionary in lookup file
logger
.
info
(
'Saving
%s
neuron lookup table in file: "
%s
"'
,
neuron
.
name
,
lookup_path
)
with
open
(
lookup_path
,
'wb'
)
as
fh
:
pickle
.
dump
(
df
,
fh
)
if
__name__
==
'__main__'
:
main
()
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